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<TITLE>CS 578 Syllabus - Fall '94</TITLE>

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<h1>Neural Networks and Connectionist Computing </h1>
TuTh 9:35-10:50am, 2241 SFLC
 <P>
Tony Martinez, 3334 TMCB, Office Hours: TuTh 3:00 - 4:00pm or by appointment
 <P>

<STRONG>Goals:</STRONG> Introduce and study the philosophy, utility, 
and models of
connectionist computing, such that students are able to propose original
research with potential follow-up in a graduate research program.  Expand
the creativity of the students in all aspects of computing.
 <P>

<STRONG>Text:</STRONG>  J. Mclelland and D. Rumelhart, Explorations 
in Parallel Distributed
Processing:  A Handbook of Models, Programs, and Exercises.  Prepared
Packet of papers at the end of each section of the notes.  You will be
expected to read the assigned literature beforeand optionally after the
scheduled lecture.
 <P>

<STRONG>Prerequisites</STRONG>  Senior or Graduate standing, 
computer architecture,
Calculus, Creativity.
 <P>

<STRONG>Lab (3346 TMCB):</STRONG> 4 Mac II's with 5MB RAM and 
40 MB hard drives.  2
DS5000 workstations with 32MB RAM and 1GB disks and 3 high speed HP
workstations. (These may be used when available but the researching
graduate students have priority on these machines).  Software for
simulations, projects, etc. will be made available.
 <P>

<STRONG>Literature:</STRONG> I have placed interesting and 
representative papers for
reference in the periodicals room of the HBLL library.  There are two
separate packets (2 copies of the first) and both are under my name.  As
needed, I will place more packets in the library.  I also have more papers
in my office which can be looked over and copied under constraint of the 15
minute rule.  I can also send for most any paper you wish through
interlibrary loan, (and will do so), but it usually takes 2-3 weeks, so
plan ahead.
 <P>

<STRONG>Grading (~):</STRONG>  Simulations and Homeworks: 30%, Midterm: 22.5%, Project:
22.5%, Final: 25% (Tue., Dec. 14,  7am-10am). Grading is on a curve and
some amount of subjectivity is allowed for attendance, participation,
perceived effort, etc.  If you think, you'll be all right 
 <P>

<STRONG>Late assignments:</STRONG>    Assignments are expected on 
time (beginning of
class on due date).  Late papers will be  marked off at 5%/school day late.
 However, if you have any unusual circumstances (sick, out of town, unique
from what all the other students have, etc.), which you inform me of, then
I will not take off any late points.  Nothing will be accepted after the
last day of class instruction.
 <P>

<STRONG>Project:</STRONG>   An indepth effort on a particular aspect of neural
networks.  A relatively extensive literature search in the area is expected
with a subsequent bibliography.  Good projects are typically as follows: 
Best:  Some of your own original thinking and proposal of a network,
learning paradigm, system, etc.  This (and other projects) are typically
well benefited by some computer simulation to bear out potential.  Very
Good:  Starting from an indepth study of some current model, strive to
extend it through some new mechanisms.  Not Bad: A study of a current model
with an indepth analysis of its strengths, weaknesses, potential, and
suggested research.  Not Good:  A description of a current model.  The
earlier you start the better.  Note that in a semester course like this,
you will have to choose a topic when we have only covered half of the
material.  That does not mean your project must cover items related to the
first half of the semester.  You should use your own initiative and the
resources available (library literature, texts, me, etc.) to peruse and
find any topic of interest to you, regardless of whether we have or will
cover it in class.  Interesting models which we will probably not have time
to cover indepth in class include: Feldman nets, Genetic algorithms,
Kohonen maps, HOTLU's, BAMs, CMAC, ASN, Cognitron, Neo-Cognitron, BolzCONS,
Michie Boxes, Cauchy Machines, Counterpropagation, Madaline II, Associative
Networks, RCE, etc, etc.
 <P>

<DL>
<DT> <STRONG>Topics and Reading Assignments</STRONG>
<DD> 1.  Intro to Neural Networks (1) *
<DD> 2.  Brain and Nervous System  (3)               Your Neural Network
<DD> 3.  Computation, VN Bottleneck, and NN Goals (1)        
<DD> 4.  Definitions, Theory, learning, applications, and    
General Mechanisms of Neural Networks (2)       
<DD> 5. Delta Rule Models - Linear associators, Perceptron, Adaline, 
 Quadric Machines, Higher Order networks, Committee Machines,          
Delta rule Simulation
and separability issues (4)
<DD> 6.  Back-Propagation (2)                Backpropagation Sim.
<DD> 7.  ASOCS (Adaptive Self-Organizing Concurrent Systems) (6)             
<DD> 8.  Midterm (1)         Project Abstract
<DD> 9.  Hopfield Networks (2)               
<DD> 10.  Boltzmann Machine (1) 
<DD> 11.  Competitive Learning, Adaptive Resonance Theory (2)                CL
Simulation
<DD> 12.  Survey of other models, implementation, future research (2)        
<DD> 13. Oral Presentations (2)              Final Project Paper
</DL>


*As a general rule, read all of the papers at then end of a section of
notes before the lecture.


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